This invention relates to a data processor which may be used in processing signals and displaying information received from a meat probe to carry out objective determinations of meat quality. The data processor of the present invention analyses feature aspects of the probe data for patterns and makes predictions of meat tenderness based upon similarities to previous samples with known measures of meat tenderness. A system which consistently predicts meat quality, particularly in respect of tenderness, would be of significant benefit to the consumer, and as well to the packing house and to the farmer. The data processor works in conjunction with a meat probe which emits radiation to excite connective tissue to fluoresce. The meat probe is designed to pick up and transmit signals relating to fluorescence and position of the probe to the data processor. The data processor then analyses the data required from the meat probe and displays the information in graphical format on a display device.
It is well known in the art that connective tissue is a major factor in variation of tenderness between different cuts of meat. Collagen, which is the dominant protein of connective tissue, emits blue-white fluorescence when excited with UV light at around the 370 nm range. There are several different biochemical types of collagen that differ in molecular structure. Of the two dominant types that occur in skeletal muscle and tendons, type I forms large unbranched fibres while type III forms small branched reticular fibres. Hence a meat probe coupled with a data processor capable of stimulating, measuring and analysing fluorescence from a cut of meat can be used in assessing meat tenderness.
The principle of connective tissues in meat fluorescing when exposed to a particular radiation wavelength has been known for some time as described by Swatland, H. J. Objective Measurement of Physical Aspects of Meat Quality, Reciprocal Meat Conference Proceedings, Vol. 42, 1989. Initial investigations in the development of a probe, which is capable of both exciting and collecting fluorescence from connective tissue in meat, are described in Swatland, H. J. Analysis of Signals from a UV Fluorescent Probe for Connective Tissue in Beef Carcasses, Computers and Electronics in Agriculture (6, 1991) 225:218 and Bidirectional Operation of a UV Fluorescent Probe for Beef Carcass Connective Tissues, Computers and Electronics in Agriculture (7, 1992) 105:300, both of Elsevier Science Publishers B. V. Amsterdam. The original probe was an adaptation of a fat depth probe used by the Danish Meat Research Institute in Denmark for measuring the depth of fat on pig carcasses. The original probe was adapted by the use of an optical fibre which was inserted in the device. The fibre was cut at an angle so that the interface optics were asymmetrical. Exciting radiation was supplied in the optic fibre from a 100 watt short arc mercury source directed through a heat absorbing filter, a red attenuation filter and a dichroic mirror. Light peaking at 225 nanometers was directed into the proximal end of the optic fibre with a microscopic objective. Fluorescence from the connective tissues in contact with the optical fibre of the probe was measured through the dichroic mirror at the proximal end of the fibre with a flat response silica detector and a radiometer. The dichroic mirror was used as a chromatic beam splitter to separate the outgoing excitation light at 225 nanometer from the incoming fluorescent emission at a wavelength considerably greater than 225 nanometer. A depth measurement device for measuring the depth to which the probe was plunged into the carcass was provided either by an optical shaft encoder to trigger photometer measurements at set increments through the carcass, or a continuously variable analogue device, such as a potentiometer. The operation of the potentiometer can be affected by temperature.
The positioning of the glass optic fibre in the probe was also suggested, instead of being cut at an angle, of being slightly bent or rounded in conjunction with a plurality of additional thin fibres as described in the article by Swatland, H. J., Bi-directional Operation of a UV Fluorescence Probe for Beef Carcass Connective Tissues Computers and Electronics in Agriculture 7(1992) 105:300. The use of the multiple fibres around the glass optic fibre was to gather additional information in respect of shape of the connective tissue as the probe passed by the connective tissue. Extensive analysis of the collected fluorescence from use of the meat probe is described in several papers by Swatland in Food Research International which include Correction for Baseline Drifting in Probe Measurements of Connective in Beef, Food Research International 26, 1993 371:374; An Anomaly in the Effective Temperature on Collagen Fluorescence in Beef, Food Research International, 26, 1993 271:276 and Correlations of Mature Beef Palatability with Optical Probing of Raw Meat, Food Research International, Vol 10, No. 4, pp 403-446, 1995. Swatland also published with others in Swatland et al., An Effective Connective Tissue on the Taste Panel Tenderness for Commercial Prime Beef Detected with a UV Fibre Optic Probe (cite to be inserted) and UV Fibre Optics Probe Measurements of Connective Tissue in Beef Correlated with Taste Panel Scores for chewieness, Food Research International, Vol 10. No. 1, pp 23-30, 1995.
Data collected from a meat probe plunged in a carcass usually includes at least two parameters: depth of insertion of the probe and level of fluorescence. Once this data has been obtained, it is necessary to process, evaluate and present it in some meaningful manner. By processing feature aspects of the data and recognizing and associating patterns in the data with previous patterns where the measure of tenderness is known, it is possible to predict tenderness of a meat sample. In addition, since data presented in table form can be difficult to comprehend, the typical method of display is to use graphical display with depth of penetration on the x axis and level of fluorescence on the y-axis. When viewing data obtained in this way, the graph forms a number of peaks and valleys of varying height and widths. The data will vary from sample to sample in amplitude and variation of amplitude from different positions on the carcass, as well as from carcass to carcass. It was thought that a comparison of the number of peaks, height of peaks, frequency of peaks and width of peaks for various samples of meat all on the same scale allowed one to assess tenderness by virtue of these characteristics. It was generally understood that a print-out of these characteristics, which shows a relatively smooth line, indicated tender meat. Presenting the above characteristics of the fluorescent data always at the same scale was believed to be more than sufficient in assessing and evaluating the information in establishing tenderness. We have now discovered that changing the scale for the representation of the data provides useful visual information in evaluating meat tenderness. It has been found that, in changing the scale, there is useful information in respect of the number of peaks, height of peaks, frequency of peaks and width of peaks where in the scale which normally accommodated tougher pieces of meat, the representation would in essence be flatline. This is useful in allowing an operator to visually assess the structure of the collagen and tenderness of the meat. In addition, we have also discovered that upon analysis of chosen aspects of the data, and comparing those aspects with information from previous cases where meat tenderness is known, it is possible to predict meat tenderness and to categorise the probed sample into a tenderness classification.
The invention provides a data processor used in the overall process of determining meat tenderness which receives, analyses and graphically displays in a dynamic format collected fluorescence emitted by connective tissue as the probe passes by such tissue during either insertion or removal of the meat probe from the meat. The data processor of the present invention also collects and calculates feature variables based on the data collected during the insertion and removal of the meat probe, and through an innovative technique using artificial intelligence and artificial neural network processing, makes a prediction of meat tenderness.
According to an aspect of the invention, a method for predicting meat quality of a meat sample by analyzing data representative of a fluorescent signal generated by a meat probe inserted in and withdrawn from said meat sample; said data being in two sets, a first set representative of said fluorescent signal generated by said probe on xe2x80x9cway inxe2x80x9d during probe insertion and a second set representative of said fluorescent signal generated by said probe on xe2x80x9cway outxe2x80x9d during probe withdrawal; said method comprising:
i) analyzing said data by use of a trained artificial neural network structure where said analysis is carried out on selected said first set of data, said second set of data or both said first and second sets of data;
ii) developing by way of said trained artificial neural network structure analyzing said selected data, a value representative of level of meat quality; and
iii) classifying level of meat quality of said meat sample according to said value.
According to a further aspect of the invention, a method of training artificial intelligence software to predict tenderness of a meat sample from a signal representative of fluorescence generated by a meat probe comprises:
i) calculating one or more fluorescent feature variables of:
total half peak width;
average half peak width per unit length;
total number of peaks;
fractional smooth length;
number of peaks per unit length;
average peak height;
half peak width;
total peak width (all peaks); and
average half peak width;
ii) independently obtaining a measure of meat tenderness of said meat sample;
iii) inputting said feature variables into artificial intelligence software;
iv) inputting said measure of meat tenderness into said artificial intelligence software;
v) teaching said artificial intelligence software to recognize patterns within said feature variables and associate said patterns with said measure of tenderness;
vi) repeating steps i) to v) above until said artificial intelligence software is able to correlate said values with said measure of meat tenderness to a value at least greater than 6.
According to a further aspect of the invention, a method of training neural network software to predict meat tenderness from a signal representative of fluorescence using the components of the signal of:
total half peak width;
average half peak width per unit length; and
at least one feature variable selected from the group consisting of:
total number of peaks number of peaks;
fractional smooth length;
number of peaks per unit length;
average peak height; and
half peak width.
According to another aspect of the invention, a method of training neural network software to predict meat tenderness from a signal representative of fluorescence using the components of the signal of:
total half peak width;
average half peak width per unit length; and
at least one feature variable selected from the group consisting of:
total number of peaks number of peaks;
fractional smooth length;
number of peaks per unit length;
average peak height; and
half peak width.